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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84093


    Title: 基於雙層詞性序列對序列模型之對話機器人;Chatbot based on two layer parts-of-speech Seq2Seq Model
    Authors: 呂家慧;Lu, Chia-Hui
    Contributors: 資訊管理學系在職專班
    Keywords: 對話機器人;詞性;序列對序列
    Date: 2020-07-28
    Issue Date: 2020-09-02 18:03:34 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 對話機器人的智能應答,除可提供快速的客戶服務,亦可以幫助企業節省大量人力,所以提供這樣的服務也代表企業的一種競爭優勢。但其效能調校工作常需耗費時間及人力成本進行維護。我們希望可以提供一種生成式對話機器人,透過深度學習大量資料建立一種自然生成對話的模型。為了提高對話機器人的回覆準確率,我們在機器訓練過程中加入了詞性維度。藉由詞性,讓機器學習了解一個句子的結構及文法,在組成答句時,能夠更貼近人類的語言。
    根據研究,生成式對話機器人多為序列對序列的深度學習模型。我們基於門控遞迴單元編碼器與解碼器組成序列對序列框架,再加入詞性,設計出四個新的詞性序列對序列。根據模型訓練後的評估結果,其中三種設計的模型都有高於基準序列對序列框架的效能表現,其中又以雙層詞性序列對序列模型的效能最為優越。
    雙層詞性序列對序列的模型,經實驗多重驗證後,應可實作於業界的對話機器人的訓練上。提升的效能,除了可降低維護人力成本外;精準的回覆客戶問題,亦可增加客戶滿意度。
    ;In this paper, I develop a deep learning model to build a chatbot. To improve the response accuracy of the chatbot, I added the parts-of-speech dimension in the model to make the machine can learn the structure and grammar of a sentence.
    This research is based on GRU Seq2Seq framework, adding parts-of-speech dimension and generate 4 new models for comparison.
    According to the evaluation results, the three models of the 1hPosSeq2Seq Model, CVPoSSeq2Seq Model, and 2LPoSSeq2Seq Model all have higher performance than the benchmark sequence-to-sequence framework. Among them, the performance of the LPoSSeq2Seq model is the most superior, with a performance improvement of 40.08 %.
    Appears in Collections:[資訊管理學系碩士在職專班 ] 博碩士論文

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